Generation Method and Device for generating anonymous dataset, and method and device for risk evaluation

ABSTRACT

An anonymous dataset generation method comprises following steps. A critical attribute set and a quasi-identifier (QID) set are acquired, and one of the critical attribute and the quasi-identifier is set as an anchor attribute. An attribute sequence and an equivalence table are generated according to the quasi-identifier set and the critical attribute set. A data cluster and a cluster table are generated according to the equivalence table. The content of the cluster table is generalized to generate and output an anonymous dataset corresponding to an original dataset. A risk evaluation method for an anonymous dataset calculates data weight to extract distinctive data and to attacking defects of the anonymous dataset according to the distinctive data, thereby enhancing a risk evaluation efficiency of the anonymous dataset.

CROSS-REFERENCE TO RELATED APPLICATIONS

This non-provisional application claims priority under 35 U.S.C. §119(a)on Patent Application No(s). 101150619 filed in Taiwan, R.O.C. on Dec.27, 2012, the entire contents of which are hereby incorporated byreference.

TECHNICAL FIELD

The disclosure relates to a method and a device for generating ananonymous dataset, and a method and a device for risk evaluation.

BACKGROUND

With the development of data digitalization, the privacy protection ofdigital data begins to attract a lot of attention, especially sensitivepersonal information of personal lifestyle, e.g. habit, interest oroccupation, as well as personal medical and health care information suchas medical history or medication information. Such sensitive personalinformation can be easily re-identified, so that personal rights andinterests will be seriously affected once the information isre-identified or leaked out.

In the past, in order to solve the problem of privacy protection ofdigital data, the random modification of data, the adding of fake data,the data perturbation or the data suppression have been used forgenerating anonymous data. However, in such conventional methods, theauthenticity and creditability of the digital data are reduced becausethe data is modified randomly or fake data is added; or, the digitaldata is excessively distorted because a part of the digital data ismodified and deleted not according to the authentic data. Therefore,usability and privacy of data cannot be achieved at the same timethrough the conventional methods.

After the anonymous data is generated, the digital data administratormay want to perform a risk evaluation for the re-identification of theanonymous data. In conventional risk evaluation method, all pieces ofthe original data are used for performing the re-identification.However, such risk evaluation method is very inefficient because of thelong and needless evaluating computation for the repetitions of theoriginal data.

SUMMARY

The disclosure relates to an anonymous dataset generation method, whichincludes following steps. A critical attribute set and aquasi-identifier (QID) set are acquired. The critical attribute setincludes at least one critical attribute, the quasi-identifier setincludes a plurality of quasi-identifiers, and one of the at least onecritical attribute is set as an anchor attribute. An equivalence tableis generated according to the quasi-identifier set, the criticalattribute set and an original dataset. The equivalence table includes aplurality of equivalence classes, each of the equivalence classesincludes at least one equivalence data, and each of the equivalence dataincludes a plurality of original values respectively corresponding tothe quasi-identifiers. A plurality of data clusters of a cluster table(CT) is generated sequentially according to the equivalence table, andeach of the data clusters includes at least one equivalence class.Content of the cluster table is generalized for generating andoutputting an anonymous dataset corresponding to the original dataset.The original values corresponding to the anchor attribute are maintainedoriginally in the anonymous dataset.

The disclosure relates to an anonymous dataset generation device, whichincludes a memory and a processor. The memory is used for storing dataor storing data temporarily. The processor is coupled to the memory. Theprocessor comprises an equivalence generation module, a clustergeneration module and a data generalization module.

The equivalence generation module is used for performing followingsteps. A critical attribute set and a quasi-identifier set are acquired.The critical attribute set includes at least one critical attribute, thequasi-identifier set includes a plurality of quasi-identifiers, and oneof the at least one critical attribute is set as an anchor attribute. Anequivalence table is generated according to the quasi-identifier set,the critical attribute set and an original dataset. The equivalencetable includes a plurality of equivalence classes, each of theequivalence classes includes at least one equivalence data, and each ofthe at least one equivalence data includes a plurality of originalvalues respectively corresponding to the quasi-identifiers.

The cluster generation module is used for generating a plurality of dataclusters of a cluster table sequentially according to the equivalencetable. Each of the data clusters includes at least one of theequivalence classes. The data generalization module is used forgeneralizing the content of the cluster table to generate and output ananonymous dataset corresponding to the original dataset. The originalvalues corresponding to the anchor attribute are maintained originallyin the anonymous dataset.

The disclosure relates to a risk evaluation method for evaluating ananonymous dataset generated according to an original dataset, whichincludes following steps. A plurality of appearing times respectivelycorresponding to a plurality of original values of the original datasetis acquired. A partition set and a weight table are generated accordingto a sample parameter, an anonymous parameter and the appearing times.The original dataset is divided into a plurality of data partitionsaccording to the partition set. A penetration dataset is generatedaccording to the weight table and the data partitions. The penetrationdataset includes a plurality of sample data. Each of the sample datacompares with a plurality of anonymous data of the anonymous dataset forobtaining a plurality of matching quantities respectively correspondingto the plurality of sample data. A risk evaluation result is calculatedand outputted according to the matching quantities.

The disclosure relates to a risk evaluation device used for evaluatingan anonymous dataset generated according to an original dataset. Therisk evaluation device includes a memory and a processor. The memory isused for storing data or storing data temporarily. The processor iscoupled to the memory. The processor includes a weight generationmodule, a sample generation module and a risk evaluation module.

The weight generation module is used for acquiring a plurality ofappearing times respectively corresponding to a plurality of originalvalues of the original dataset, and for generating a partition set and aweight table according to a sample parameter, an anonymous parameter andthe appearing times. The sample generation module is used for dividingthe original dataset into a plurality of data partitions according tothe partition set, and for generating a penetration dataset according tothe weight table and the data partitions. The penetration datasetincludes a plurality of sample data. The risk evaluation module is usedfor comparing each sample data with a plurality of anonymous data of theanonymous dataset to obtain a plurality of matching quantitiesrespectively corresponding to the plurality of sample data, and forcalculating and outputting a risk evaluation result according to thematching quantities.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will become more fully understood from thedetailed description given herein below for illustration only and thusdoes not limit the present disclosure, wherein:

FIG. 1A is a block diagram of an anonymous dataset generation deviceaccording to an embodiment of the disclosure;

FIG. 1B is a block diagram of the anonymous dataset generation deviceaccording to an embodiment of the disclosure;

FIG. 2 is a flow chart of an anonymous dataset generation methodaccording to an embodiment of the disclosure;

FIG. 3 is a flow chart of step S110 according to an embodiment of thedisclosure;

FIG. 4 is a flow chart of step S120 according to an embodiment of thedisclosure;

FIG. 5 is a flow chart of step S123 according to an embodiment of thedisclosure;

FIG. 6A is a schematic diagram of a taxonomy tree according to anembodiment of the disclosure;

FIG. 6B is a schematic diagram of a taxonomy tree according to anembodiment of the disclosure;

FIG. 7 is a flow chart of step S130 according to an embodiment of thedisclosure;

FIG. 8 is a flow chart of step S131 according to an embodiment of thedisclosure;

FIG. 9 is a flow chart of step S206 according to an embodiment of thedisclosure;

FIG. 10 is a flow chart of step S140 according to an embodiment of thedisclosure;

FIG. 11 is a flow chart of step S145 according to an embodiment of thedisclosure;

FIG. 12 is a block diagram of a risk evaluation device of the anonymousdataset according to an embodiment of the disclosure;

FIG. 13 is a flow chart of a risk evaluation method of the anonymousdataset according to an embodiment of the disclosure;

FIG. 14 is a flow chart of the step S520 according to an embodiment ofthe disclosure;

FIG. 15 is a flow chart of the step S524 according to an embodiment ofthe disclosure;

FIG. 16 is a flow chart of the step S530 according to an embodiment ofthe disclosure; and

FIG. 17 is a flow chart of the step S540 according to an embodiment ofthe disclosure.

DETAILED DESCRIPTION

In the following detailed description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the disclosed embodiments. It will be apparent,however, that one or more embodiments may be practiced without thesespecific details. In other instances, well-known structures and devicesare schematically shown in order to simplify the drawing.

An anonymous dataset generation method and device and a risk evaluationmethod and device of the anonymous dataset are provided by thedisclosure. The anonymous dataset generation device is used forperforming the anonymous dataset generation method. The risk evaluationdevice is used for performing the risk evaluation method.

FIG. 1A is a block diagram of an anonymous dataset generation deviceaccording to an embodiment of the disclosure. An anonymous datasetgeneration device 30 includes a processor 31 and a memory 38. The memory38 is used for storing data or storing data temporarily. The processor31 comprises an equivalence generation module 32, a cluster generationmodule 34 and a data generalization module 36. The processor 31 canperform each step of the anonymous dataset generation method to generatean anonymous dataset according to an original dataset. The originaldataset, the anonymous dataset, an equivalence table, which is requiredfor generating the anonymous dataset, and a cluster table can be a datatable stored or stored temporarily in the memory 38 or in a database 50of a computer.

FIG. 2 is a flow chart of the anonymous dataset generation methodaccording to an embodiment of the disclosure. The anonymous datasetgeneration method can de-identify the original dataset and generate theanonymous dataset conformed to a k-anonymity privacy protectionlimitation. K is a positive integer.

Firstly, the equivalence generation module 32 acquires a criticalattribute set and a quasi-identifier (QID) set (step S110). The criticalattribute set includes at least one critical attribute, and thequasi-identifier set includes a plurality of quasi-identifiers. Thecritical attribute set can be a subset of the quasi-identifier set. Oneof the at least one critical attribute or one of the quasi-identifiersis set as an anchor attribute. Then, the equivalence generation module32 generates an equivalence table according to the quasi-identifier set,the critical attribute set and the original dataset (step S120).

The equivalence table includes a plurality of equivalence classes. Eachof the equivalence classes includes at least one equivalence data, andeach equivalence data includes a plurality of original valuesrespectively corresponding to the quasi-identifiers. The criticalattribute can be set according to various usages of a user who desiresto use the original dataset. It indicates that the data related to thecritical attributes should be maintained as much as possible in order toensure the correctness when the data is used subsequently. Thequasi-identifiers can be set by an administrator of the originaldataset. It indicates that the data related to the quasi-identifiers issensitive data.

The cluster generation module 34 generates a plurality of data clustersof the cluster table according to the equivalence table sequentially(step S130). Each of the data clusters includes at least one of theequivalence classes. Then, the data generalization module 36 performsdata generalization on the cluster table for generating and outputtingthe anonymous dataset corresponding to the original dataset (step S140).The original values corresponding to the anchor attribute are maintainedoriginally in the anonymous dataset.

The most important one of the at least one critical attribute is set asthe anchor attribute. In the de-identification, the data related to theanchor attribute is not changed. Therefore, in the anonymous dataset,the original values corresponding to the anchor attribute are maintainedoriginally in order to prevent the anonymous dataset from the excessivedistortion caused by the de-identification. The detail of the method inFIG. 2 is described as below.

Refer to FIG. 1B, it is a block diagram of the anonymous datasetgeneration device according to an embodiment of the disclosure. Theequivalence generation module 32 can include an equivalence compositionunit 321, an attribute sequence unit 322, a construction unit 323, acoding unit 324 and a sorting unit 325, and be used for perform thesteps S110 and S120.

Please refer to FIG. 3, it is a flow chart of the step S110 according toan embodiment of the disclosure. The equivalence composition unit 321 ofthe equivalence generation module 32 reads the quasi-identifier set andthe critical attribute set from a configuration file or a user interface(UI) (step S111). The critical attribute set can be a subset of thequasi-identifier set. According to an embodiment, the anonymous datasetgeneration device 30 provides a graphical user interface (GUI) for auser to set the critical attribute set through the GUI, or for anadministrator to set the quasi-identifier set through the GUI. Thecritical attribute set or the quasi-identifier set, set by the user orthe administrator, can be automatically stored in the configuration fileby the GUI.

After reading the quasi-identifier set and the critical attribute set,the equivalence composition unit 321 determines whether all of the atleast one critical attribute belong to the quasi-identifier set (stepS112). In other words, the equivalence composition unit 321 determineswhether the quasi-identifier set includes the critical attribute set, ordetermines whether any of the quasi-identifiers in the quasi-identifierset is the same as the critical attribute.

If any one of the at least one critical attribute does not belong to thequasi-identifier set, the equivalence composition unit 321 deletes thecritical attribute, which do not belong to the quasi-identifier set,from the critical attribute set (step S113). Therefore, each criticalattribute set by the user belongs to the quasi-identifier set. Thequasi-identifier set includes the quasi-identifiers used as the criticalattributes, and other quasi-identifiers.

According to an embodiment, the original dataset has three dataattributes, namely a direct identifier, a sensitive attribute and aquasi-identifier. The direct identifier indicates a single originalvalue used for directly identifying an original attribute of a specificperson. The direct identifier is, for examples, an identity card number,an employee number or a person name. The definition of the sensitiveattribute is related to the usage intention. Generally, the sensitiveattribute is related to personal privacy. The quasi-identifier indicatesa data attribute which neither belongs to the direct identifier norsensitive attribute. The quasi-identifier set is set by theadministrator according to various usage intentions. Various usageintentions require different sensitive attributes. For example, in theregional revenue analysis, the personal medical history is sensitivedata.

In other words, the quasi-identifier is what is non-sensitive, does notleak out personal privacy, and is confirmed by the administrator. Byperforming the step S113, the sensitive data attribute can be preventedfrom being leaked out when a user sets the sensitive data attribute asthe critical attribute carelessly or intentionally.

FIG. 4 is a flow chart of the step S120 according to an embodiment ofthe disclosure. The equivalence composition unit 321 extracts theequivalence table from the original dataset according to thequasi-identifier set, and each of the equivalence classes includes acorresponding quantity of the equivalence data (step S121). The originaldataset includes a plurality of the original data. Each original dataincludes a plurality of original values respectively corresponding tothe original attributes, and the quasi-identifiers are a part of theoriginal attributes. The equivalence composition unit 321 can find outthe original values in all of the plurality of original datacorresponding to the quasi-identifier set.

The equivalence composition unit 321 determines whether any originalattribute is a direct identifier. The equivalence composition unit 321determines which original attribute is the direct identifier, and doesnot add the direct identifier and the corresponding original attributeto the equivalence table. According to an embodiment, if onequasi-identifier or one critical attribute is a direct identifier, thedirect identifier is deleted from the quasi-identifier set and thecritical attribute set.

Assume that an American adult data is stored in the original dataset,and an example of the original dataset is shown in Table 1 as below.

TABLE 1 Work class Sex Data number Age Private Male 96509 23State-government Male 335005 51 . . . Non-enterprise Female 243768 40employee Enterprise employee Female 151910 33 Private Male 150683 43Local-government Female 89813 29 Private Male 150234 43

The four original attributes in the original dataset in Table 1 are“work class”, “sex”, “data number” and “age”. The data number is adirect identifier or a sensitive attribute but not a quasi-identifier.Assume that the critical attribute is “sex” and the quasi-identifiersare “work class”, “sex” and “age”, then the equivalence composition unit321 can obtain the original values corresponding to “work class”, “sex”and “age”, in the original dataset.

According to the arrangement of the original values of each originaldata, the equivalence composition unit 321 can provide variousequivalence classes. All the original values corresponding to the samequasi-identifier are grouped into the same equivalence class. Forexample, the two original data respectively having the data numbers“150683” and “150234” have the same original values corresponding to thequasi-identifiers, i.e. “private”, “male” and “43”, so that the originalvalues are grouped into the same equivalence class. This equivalenceclass includes at least these two equivalence data.

After all the equivalence classes are formed by the equivalencecomposition unit 321 according to the original values, all theequivalence classes are stored as an equivalence table. Then, theequivalence composition unit 321 computes a quantity of the originaldata corresponding to each of the equivalence classes to obtaincorresponding quantities, and the corresponding quantities are stored inthe equivalence table.

The attribute sequence unit 322 generates an attribute sequenceaccording to the quasi-identifier set and the critical attribute set(step S122). The attribute sequence unit 322 employs an attributesequence rule to sort all the quasi-identifiers in order to generate theattribute sequence. The attribute sequence rule includes a first rule, asecond rule, a third rule and a fourth rule described below. Aftersorting the quasi-identifiers, the critical attribute or thequasi-identifier, having the highest priority, are set as the anchorattribute.

According to an embodiment, the critical attribute set can be an emptyset. In other words, the user does not have to specify the criticalattribute. If none of the critical attribute is specified, the attributesequence unit 322 sorts all the quasi-identifiers according to theattribute sequence rule, and sets the first quasi-identifier in theattribute sequence as the anchor attribute.

The first rule requires that the priority of the critical attribute ishigher than the priority of the quasi-identifier not belonging to thecritical attributes. The second rule requires that the priority of thequasi-identifier belonging to a categorical type attribute is higherthan the priority of the quasi-identifier belonging to a numeric typeattribute. If the original value corresponding to the quasi-identifieris numeral, the quasi-identifier belongs to the numeric type attribute.If the original value corresponding to the quasi-identifier is acharacter or a string, the quasi-identifier belongs to the categoricaltype attribute.

The third rule requires that the priority of the quasi-identifiercorresponding to a shorter height of a taxonomy tree is higher than thepriority of the quasi-identifier corresponding to a taller height of thetaxonomy tree. Each of the quasi-identifiers of the categorical typeattribute corresponds to an independent taxonomy tree. In general, themore complicated the original value, the higher the height of thetaxonomy tree is. The fourth rule requires that the priority of thequasi-identifier corresponding to a lower original value variance ishigher than the priority of the quasi-identifier corresponding to ahigher original value variance. The original value variance is a numberof types of the original values corresponding to the quasi-identifier.For example, the original values corresponding to “sex” are “male” and“female”, so that the original value variance is two.

For example, “sex” is the critical attribute, so that its priority ishigher than the priorities of “work class” and “age” in the attributesequence. Because “work class” belongs to the categorical type attributeand “age” belongs to the numeric type attribute, the priority of “workclass” is higher than the priority of “age” in the attribute sequence.Accordingly, the outputted attribute sequence is “sex, work class andage”, and the anchor attribute is “sex”.

According to each of the original values of each of thequasi-identifiers, the construction unit 323 generates a plurality ofvalue codes corresponding to the original values (step S123). The detailof the step S123 is described as follows.

FIG. 5 is a flow chart of the step S123 according to an embodiment ofthe disclosure. Firstly, the construction unit 323 determines whetherthe quasi-identifiers belong to the numeric type attribute or thecategorical type attribute (step S1231). For the quasi-identifiers ofthe numeric type attribute, the corresponding original values can bedirectly set as the corresponding value codes (step S1232). For example,each of the original values corresponding to “age” can be directly setas the value code. Therefore, the value code corresponding to theoriginal value of “43” is 43.

For the quasi-identifiers of the categorical type attribute, theconstruction unit 323 generates the taxonomy tree according to thecorresponding original values, and encodes the corresponding originalvalues via the taxonomy tree in order to obtain the corresponding valuecodes (step S1233). The detail of the taxonomy tree is described asfollows.

FIGS. 6A and 6B illustrate the taxonomy trees corresponding to thequasi-identifiers of “sex” and “work class” respectively. Leaf nodes ofthe taxonomy trees are all the possible original values corresponding tothe quasi-identifier. For example, the original values corresponding to“sex” are “male” and “female”, the original values corresponding to“work class” are “private”, “non-enterprise employee”, “enterpriseemployee”, “federal-government”, “local-government”, “state-government”,“no-pay” and “never-worked”.

The construction unit 323 generates the value codes for all the leafnodes sequentially. For example, double-bit decimal numbers aregenerated from “00”. Moreover, each of the value codes of the taxonomytree is reset. As shown in FIGS. 6A and 6B, the value codescorresponding to the original values “female” and “male” are “00” and“01” respectively, and the value codes corresponding to the originalvalues “private”, “non-enterprise employee”, “enterprise employee”,“federal-government”, “local-government”, “state-government”, “no-pay”and “never-worked” are “00” to “07” respectively. According to anembodiment, the structure of the taxonomy tree can be setup manually andpredeterminedly and stored in the configuration file. The constructionunit 323 can read the required taxonomy tree from the configuration fileand number the taxonomy tree.

After generating the value codes according to each of the originalvalues of the quasi-identifiers, the coding unit 324 encodes theequivalence classes according to the attribute sequence and the valuecodes for generating a plurality of equivalence codes (step S124). Thecoding unit 324 reads the equivalence classes one by one, sorts thevalue codes of the original values corresponding to the read equivalenceclass according to the attribute sequence, and then obtains theequivalence code. For example, the equivalence codes of the equivalenceclasses of “male”, “private” and “43” are combined to form a decimalnumber “010043” composed of “01”, “00” and “43”.

After all the equivalence codes are generated, the sorting unit 325sorts the equivalence classes of the equivalence table according to theequivalence codes, and outputs the sorted equivalence table (step S125).The sorting unit 325 can sort the equivalence codes from the smallestone to the largest one, so that the equivalence classes corresponding tothe similar original values are adjacent to each other. Thus, thereadability of the equivalence table and the rate of generating theanonymous dataset according to the equivalence table subsequently may beenhanced.

An example of the generated equivalence table is shown in Table 2 below.

TABLE 2 Equivalence Corresponding code Sex Work class Age quantity000140 Female Non-enterprise 40 1 Employee 000233 Female EnterpriseEmployee 33 2 000429 Female Local-government 29 3 010023 Male Private 232 010043 Male Private 43 5 010351 Male Federal-government 51 1 010551Male State-government 51 1

In this way, the equivalence generation module 32 automaticallygenerates the equivalence table according to the critical attribute setdefined by the user. Then, the cluster generation module 34 generatesthe cluster table according to the equivalence table. The equivalencetable includes a plurality of the data clusters, and each of the dataclusters includes a cluster code and at least one of the equivalenceclasses.

In order to ensure the generated anonymous dataset is conformed to thek-anonymity privacy protection limitation, the anonymous datasetgeneration device 30 employs an anonymous parameter k to perform itsverification when the anonymous dataset is generated. The anonymousdataset includes a plurality of anonymous data, and each of theanonymous data includes a plurality of third attribute valuesrespectively corresponding to the quasi-identifiers. K-anonymity meansthat there are k or more than k pieces of anonymous data correspondingto the same third attribute value, in the anonymous dataset. The higherthe value of k, the higher the degree of de-identification is. Theanonymous parameter can be set by the user or the administrator throughthe GUI, and can be read from the configuration file.

FIG. 7 is a flow chart of the step S130 according to an embodiment ofthe disclosure. Firstly, the cluster generation module 34 adds theequivalence classes to the data clusters according to the correspondingquantities sequentially (step S131), and then determines whether a totalquantity of the equivalence data in any one of the data clusters issmaller than the anonymous parameter (step S132).

When a total quantity of the equivalence data in any one of the dataclusters is smaller than the anonymous parameter, the cluster generationmodule 34 can set this data cluster corresponding to the total quantitysmaller than the anonymous parameter, to be a first cluster (step S133),and set a previous data cluster, which is before the first cluster, as asecond cluster (step S134). Assume that the data clusters in the clustertable are sorted from the smallest one to the largest one according tothe cluster codes, then the first cluster and the second cluster are thetwo consecutive data clusters in the cluster table, and the cluster codeof the first cluster is smaller than the cluster code of the secondcluster.

The cluster generation module 34 determines whether the original valuescorresponding to the anchor attribute, in the first cluster and thesecond cluster are the same (step S135). When the original valuescorresponding to the anchor attribute, in the first cluster and thesecond cluster are the same, the first cluster and the second clusterare merged (step S136), so that the quantity of the equivalence classesin the merged data cluster is bigger than the anonymous parameter.

More specifically, in order to embody the k-anonymity privacy protectionlimitation, the cluster generation module 34 also makes sure that thereare k or more than k pieces of the equivalence data in each of thegenerated data clusters as much as possible in the step S131.

FIG. 8 is a flow chart of the step S131 according to an embodiment ofthe disclosure. The cluster generation module 34 reads all theequivalence classes sequentially (step S201) in order to add all theequivalence data to the cluster table sequentially.

The cluster generation module 34 determines whether the read equivalenceclass is the first equivalence class in the equivalence table (stepS202). If yes, steps S203 to S206 are performed; otherwise, step S207 toS212 are performed.

The cluster generation module 34 can add a temporary cluster and set thetemporary cluster to correspond to one of the data clusters (step S203).More specifically, if the read equivalence class is the firstequivalence class in the equivalence table, it indicates that none ofthe data clusters is generated herein. Therefore, when the quantity ofthe equivalence data in the temporary cluster is bigger than or equal tothe anonymous parameter, the temporary cluster can be stored as thefirst data cluster. Assume that the cluster code of the first datacluster equals 1; then the temporary cluster can be set to correspond tothe data cluster with the cluster code equaled to 1 in the step S203.According to this principle, the cluster generation module 34 cangenerate the data clusters one by one.

The cluster generation module 34 records the original valuecorresponding to the anchor attribute, in the first equivalence class tobe a current anchor value (step S204). Take Table 2 as an example, theoriginal value “female” corresponding to the anchor attribute “sex” isrecorded as the current anchor value. Then, the cluster generationmodule 34 accumulates an accumulative quantity according to thecorresponding quantity of the read equivalence classes (step S205), andadds the read equivalence classes to the temporary cluster according tothe anonymous parameter and the accumulative quantity (step S206).

Assume that the original value of the accumulative quantity is 0, in thestep S205, a sum of the current accumulative quantity and thecorresponding quantity of the read equivalence classes can be set as anupdated accumulative quantity. In an example of the first equivalenceclass with the equivalence code “000140” in Table 2, the correspondingquantity is 1, and the accumulative quantity is 1 (0+1=1).

On the other hand, when the read equivalence class is not the firstequivalence class in the equivalence table, the cluster generationmodule 34 can further determine whether the original valuescorresponding to the anchor attribute, in the read equivalence class arethe same as the recorded current anchor value (step S207). If theoriginal values corresponding to the anchor attribute, in the readequivalence classes are the same as the current anchor value, the stepsS205 and S206 can also be performed for adding the read equivalenceclass to the temporary cluster.

For example, when the read equivalence class is the second equivalenceclass (the equivalence class with the equivalence code “000233”) inTable 2, and when the original value corresponding to the anchorattribute “sex,” in the second equivalence class is “female,” theoriginal value “female” of the second equivalence class is the same asthe current anchor value “female”. Thus, the steps S205 and S206 can beperformed on the second equivalence class. According to an embodiment,the current anchor value records the corresponding value code. Thecluster generation module 34 can directly compare the recorded valuecode with the value code corresponding to the anchor attribute, i.e.compare the recorded value code with the first two numerals of the readequivalence code. The detail of the step S206 is described as follows.

FIG. 9 is a flow chart of the step S206 according to an embodiment ofthe disclosure. The cluster generation module 34 determines whether theaccumulative quantity is smaller than the anonymous parameter (stepS301). When the accumulative quantity is smaller than the anonymousparameter, the cluster generation module 34 adds all the equivalencedata of the read equivalence class to the temporary cluster (step S302),and then reads a next equivalence class. Assume that the anonymousparameter is 3, and that the accumulative quantity accumulated accordingto the currently read equivalence class (the first equivalence class)is 1. It indicates that even though all the equivalence data of thecurrently read equivalence class are added to the temporary cluster, thecorresponding accumulative quantity does not achieve the number k of thek-anonymity yet. Thus, all the equivalence data of the currently readequivalence class can be added to the temporary cluster.

However, the content of the anonymous data will be distorted excessivelyif too many equivalence classes are merged. Thus, when the accumulativequantity is not smaller than the anonymous parameter, the clustergeneration module 34 further determines whether the accumulativequantity is smaller than twice of the anonymous parameter (step S303).As a result, the accumulative quantity may not become far bigger thanthe k-anonymity after the equivalence classes are added to the temporarycluster, which has not achieved the number k of the k-anonymity yet.

When the accumulative quantity is equal to the anonymous parameter, orwhen the accumulative quantity is bigger than the anonymous parameterand is smaller than twice of the anonymous parameter, the clustergeneration module 34 can add all the equivalence data of the readequivalence class to the temporary cluster (step S304), and store thetemporary cluster as the corresponding data cluster (step S305). Herein,even though all the equivalence data of the currently read equivalenceclass are added to the temporary cluster, the accumulative quantity isnot far bigger than the anonymous parameter.

After the temporary cluster is stored as the corresponding data cluster,the temporary cluster and the accumulative quantity can be initialized,and the initialized temporary cluster is set to correspond to a nextdata cluster after the stored data cluster (step S306). For example,when the original temporary cluster corresponds to the data cluster withthe cluster code of 1, the cluster generation module 34 can set theinitialized temporary cluster to correspond to the data cluster with thecluster code of 2. Thus, the temporary cluster is emptied after theinitialization, and the original value of the accumulative quantity isset as 0.

Take the second equivalence class (the equivalence class with theequivalence code 000233) in Table 2 as an example, the correspondingquantity is 2 and the accumulative quantity is 3 (1+2=3=k). Because theaccumulative quantity equals to the anonymous parameter, all theequivalence data of the second equivalence class can be added to thetemporary cluster, and the temporary cluster including the equivalencedata of the first and the second equivalence classes can be stored asthe data cluster with the cluster code 1. Then, the cluster generationmodule 34 initializes the temporary cluster and the accumulativequantity, sets the initialized temporary cluster to correspond to thecluster code 2, and then reads a next equivalence class.

When the accumulative quantity is bigger than or equal to twice of theanonymous parameter, the cluster generation module 34 performs stepsS307 to S313 in order to add all the equivalence data of the currentlyread equivalence class to the cluster table properly.

The cluster generation module 34 divides all the equivalence data of thecurrently read equivalence class into a first group and a second group(step S307). The first group includes at least one of the equivalencedata, and the second group includes the remaining equivalence data inthe read equivalence class. A quantity of the equivalence data in thesecond group is equal to or bigger than the anonymous parameter.

According to an embodiment, a quantity of the equivalence data in thefirst group equals to the anonymous parameter minus the accumulativequantity before the accumulation. Take the fifth equivalence class (theequivalence class with the equivalence code 010043) in Table 2 as anexample, the accumulative quantity before the accumulation is 2, thecluster code corresponding to the temporary cluster is 3, thecorresponding quantity of the read equivalence class is 5. Therefore,the accumulative quantity after the accumulation is 7. Herein, the topone (3−2=1) equivalence data can be extracted from the read equivalenceclass for being set as the first group, and the remaining four (5−1=4)equivalence data can be set as the second group.

The cluster generation module 34 can add the equivalence data of thefirst group to the temporary cluster (step S308), and then store thetemporary cluster as the corresponding data cluster (step S309).Subsequently, the temporary cluster and the accumulative quantity can beinitialized, and the initialized temporary cluster can be set tocorrespond to a next data cluster after the stored data cluster (stepS310).

Then, the cluster generation module 34 adds the equivalence data of thesecond group to the initialized temporary cluster (step S311). Becausethe quantity of the equivalence data of the second group is equal to orbigger than the anonymous parameter, after the equivalence data of thesecond group are added to the initialized temporary cluster, thetemporary cluster with the second group can be directly stored as thecorresponding data cluster (step S312). Similarly, after storing thedata cluster, the cluster generation module 34 can initialize thetemporary cluster and the accumulative quantity, and then set theinitialized temporary cluster to correspond to a next data cluster afterthe data cluster with the second group (step S313).

For example, after the temporary cluster is stored as the data clusterwith the cluster code 3, the temporary cluster and the accumulativequantity are initialized, and then the initialized temporary cluster isset to correspond to the cluster code 4. Subsequently, the five piecesof equivalence data of the second group are added to the temporarycluster corresponding to the cluster code 4, and the temporary clusteris directly stored as the data cluster with the cluster code 4. Further,the temporary cluster and the accumulative quantity are initializedagain, and the initialized temporary cluster is set to correspond to thecluster code 5.

In other words, if the accumulative quantity is far bigger than theanonymous parameter after the equivalence class is added to the currenttemporary cluster, the cluster generation module 34 can divide thisequivalence class and store the divided equivalence class as a pluralityof corresponding data clusters.

According to an embodiment, if the quantity of the equivalence data inthe second group is bigger than twice of the anonymous parameter, thequantity of the equivalence data in the second group will be maintained,and no additional division of the equivalence classes will be performed.

Moreover, the accumulative quantity can be recorded in the cluster tablewhen each of the equivalence classes is added to the temporary cluster.The accumulative quantity herein is the total quantity of theequivalence data in the temporary cluster after the equivalence classesare added to the temporary cluster. For example, the total quantity is 1(0+1=1) when the first equivalence class in Table 2 is added to thetemporary cluster, and the total quantity is 3 (1+2=3) when the secondequivalence class in Table 2 is added to the temporary cluster.

By performing the steps S301 to S313 mentioned above, the clustergeneration module 34 can generate the corresponding data clustersaccording to the read equivalence classes, and the generated dataclusters are conformed to the k-anonymity privacy protection limitation.

Refer to FIG. 8, when the read equivalence class is not the firstequivalence class, and when the original value corresponding to theanchor attribute, in the read equivalence class is different from thecurrent anchor value, steps S208 to S213 can be performed. For example,when the read equivalence class is the fourth equivalence class (theequivalence class with the equivalence code 010023) in Table 2, and theoriginal value corresponding to the anchor attribute “Sex,” in the fifthequivalence class is “Male,” which is different from the current anchorvalue of “Female”. Thus, the steps S208 to S213 can be performed for thefifth equivalence class.

If the original value corresponding to the anchor attribute, in thecurrently read equivalence class is different from the current anchorvalue, it indicates that the content of the currently read equivalenceclass is very different from the content of the previous equivalenceclass. Therefore, these two equivalence classes are not added to thesame data cluster. The cluster generation module 34 can store thetemporary cluster as the corresponding data cluster (step S208),initialize the temporary cluster and the accumulative quantity, and setthe initialized temporary cluster to correspond to a next data clusterafter the stored data cluster (step S209). The cluster generation module34 can further add the read equivalence class to the initializedtemporary cluster according to the anonymous parameter and thecorresponding quantity (step S210).

The cluster generation module 34 adds the currently read equivalenceclass and the previous equivalence class to the different data clustersrespectively, for preventing the original value corresponding to theanchor attribute, from being modified when the data generalization isperformed subsequently. The cluster generation module 34 can perform thesteps S301 to S313 to add the read equivalence class to the initializedtemporary cluster, and whereby the steps S301 to S313 will not bedescribed herein again.

Then, the cluster generation module 34 accumulates the initializedaccumulative quantity according to the corresponding quantity of theread equivalence class (step S211), and records the original valuecorresponding to the anchor attribute, in the read equivalence class tobe the current anchor value (step S212). For example, after the fourthequivalence class in Table 2 is added to the initialized temporarycluster, the accumulative quantity is updated to the correspondingquantity 2 of the fourth equivalence class, and the original value“Male” of the new anchor attribute or the value code of this original isrecorded as the new current anchor value.

Whenever each of the equivalences class is processed completely, thecluster generation module 34 can determine whether the read equivalenceclass is the last equivalence class in the equivalence table (stepS213), in order to determine whether to read a next equivalence class.By performing the steps S131 to S135, the steps S201 to S213, and thesteps S301 to S313, the cluster generation module 34 can generate thecluster table according to the equivalence table sequentially.

An example of the generated cluster table is shown in Table 3 below.

TABLE 3 Cluster Accumulative Sex Work class Age code quantity FemaleNon-enterprise 40 1 1 Employee Female Enterprise Employee 33 1 3 FemaleLocal-government 29 2 3 Male Private 23 3 2 Male Private 43 3 3 MalePrivate 43 4 4 Male Federal-government 51 4 6 Male State-government 51 46

Herein, the accumulative quantity in Table 3 is generated after theequivalence data is added.

After the cluster table is obtained, the data generalization module 36generalizes the content of the cluster table to generate the anonymousdataset. The detailed operation of the data generalization module 36 isdescribed as follows.

FIG. 10 is a flow chart of the step S140 according to an embodiment ofthe disclosure. The data generalization module 36 reads all theequivalence classes of all the data clusters in the cluster tablesequentially (step S141), and determines whether the read equivalenceclass is the first equivalence class in the cluster table (step S142).When the read equivalence class is the first equivalence class in thecluster table, the first equivalence class can be set as a temporarygeneralized model (step S143). The temporary generalized model includesa plurality of first attribute values respectively corresponding to thequasi-identifiers, and the original values of the first attribute valuescan be set as the original values of the first data cluster.

If the read equivalence class is not the first equivalence class in thecluster table, the data generalization module 36 further determineswhether the read equivalence class is the same as the cluster codecorresponding to the temporary generalized model (step S144). If theread equivalence class is the same as the cluster code corresponding tothe temporary generalized model, it indicates that both the readequivalence class and the temporary generalized model belong to the samedata cluster. Therefore, the data generalization module 36 can searchfor a smallest generalized model between the read equivalence class andthe temporary generalized model (step S145), and store the smallestgeneralized model as an updated temporary generalized model (step S146).

The smallest generalized model can be regarded as a union set betweenthe read equivalence class and the temporary generalized model. Thesmallest generalized model includes a plurality of second attributevalues respectively corresponding to the quasi-identifiers, and thecontent of the second attribute value include both the correspondingfirst attribute value and the original value. The detail for searchingthe smallest generalized model will be described later.

On the contrary, if the read equivalence class is different from thecluster code corresponding to the temporary generalized model, itindicates that the read equivalence class and the temporary generalizedmodel belong to the different data clusters respectively. Therefore, thedata generalization module 36 can store the current temporarygeneralized model in the anonymous dataset (step S147), and set the readequivalence class as a new temporary generalized model (step S148).

Each of the anonymous data includes a plurality of the third attributevalues respectively corresponding to the quasi-identifiers. The datageneralization module 36 can store the second attribute values of thecurrent temporary generalized model as the third attribute values of theanonymous data correspondingly. The data generalization module 36initializes the temporary generalized model, sets the read equivalenceclass as a new temporary generalized model, and returns to the step S141to read a next equivalence class.

On the other hand, the detail for searching the smallest generalizedmodel is described as follows. FIG. 11 is a flow chart of the step S145according to an embodiment of the disclosure. The data generalizationmodule 36 sets all the quasi-identifiers as a current identifiersequentially (step S1451), one by one inspects the original values ofthe read equivalence class corresponding to the currentquasi-identifier, and one by one inspects the first attribute values ofthe temporary generalized model corresponding to the currentquasi-identifier.

The data generalization module 36 determines whether the original valueof the read equivalence class and the first attribute value of thetemporary generalized model, which correspond to the current identifier,are the same (step S1452). When the original value and the firstattribute value are the same, the first attribute value corresponding tothe current identifier can be set as the second attribute value of thesmallest generalized model (step S1453), and the second attribute valueis stored.

For example, the first attribute value and the original value, whichcorrespond to the quasi-identifiers “sex” and “work class”, in thefourth equivalence class “male, private, 23, 3, 2” and the fifthequivalence class “male, private, 43, 3, 3” in Table 3 are the same. Thefirst attribute value or the original value, which are the same, can beset as the second attribute value directly.

When the first attribute value and the original value, which correspondto the current identifier, are different, the data generalization module36 further determines whether the current identifier belongs to anumeric type attribute or a categorical type attribute (step S1454). Ifthe current identifier belongs to the numeric type attribute, ageneralized value range can be generated according to the firstattribute value and the original value, which correspond to the currentidentifier, and the generalized value range can be set as the secondattribute value of the smallest generalized model (step S1455).

For example, when the fourth equivalence class in Table 3 is processed,the data generalization module 36 sets the original value of the fourthequivalence class as the first attribute value of the temporarygeneralized model. When the fifth equivalence class in Table 3 isprocessed, the first attribute value “23” and the original value “43” ofthe read equivalence class, which correspond to the quasi-identifier“age”, are different. Herein, the data generalization module 36generates a smallest value range including both the first attributevalue and the original value, and set the smallest value range as thegeneralized value range. In this example, the generalized value range of[23-43] can be generated, and set as the second attribute value, and thesecond attribute value is stored.

Assume that the first attribute value is a value range, and that theoriginal value is in this value range. The data generalization module 36sets the first attribute value as the generalized value range directly,and sets the generalized value range as the second attribute value. Forexample, when the first attribute value is [23-43] and the correspondingoriginal value is “23”, the second attribute value is the same as thefirst attribute value.

If the current identifier belongs to the categorical type attribute, thedata generalization module 36 generates a generalized string accordingto the taxonomy tree, the first attribute value and the original value,which correspond to the current identifier, and then sets thegeneralized string as the second attribute value of the smallestgeneralized model (step S1456). Similarly, the data generalizationmodule 36 can generate a string having a meaning capable of coveringboth the first attribute value and the original value, and can set thisstring as the generalized string. Briefly, the generalized string can befound according to the correlation of the nodes corresponding to thefirst attribute value and the original value, in the taxonomy tree.

If the node corresponding to the first attribute value, in the taxonomytree is not a leaf node, and the leaf node corresponding to the originalvalue belongs to a sub-tree based on the node (set as a root node)corresponding to the first attribute value, the data generalizationmodule 36 can set the first attribute value as the generalized stringdirectly. For example, after the seventh equivalence class “male,federal-government, 51, 4, 6” in Table 3 is processed, the firstattribute value corresponding to the quasi-identifier work class, in thetemporary generalized model is “pay”. Moreover, the correspondingoriginal value in the eighth equivalence class “male, state-government,51, 4, 6” in Table 3 is “state-government”. In the taxonomy tree shownin FIG. 6B, the leaf node corresponding to “state-government” belongs tothe sub-tree based on the root node corresponding to “pay”. Therefore,“state-government” belongs to “pay”, i.e. “pay” covers“state-government”, and “pay” can be set as the second attribute value.

If the node corresponding to the first attribute value, in the taxonomytree is a leaf node, or if the node corresponding to the original valuedoes not belong to the sub-tree based on the root node corresponding tothe first attribute value, the data generalization module 36 can set avalue corresponding to the node (i.e. a lowest common parent node)corresponding to the first attribute value and the original value, to bea generalized string. In other words, a smallest sub-tree based on thenode corresponding to the first attribute value and the original valueis found, and then the value corresponding to the root node of thesmallest sub-tree is set as the second attribute value.

For example, after the data generalization module 36 processes the firstequivalence class “female, non-enterprise employee, 40, 1, 1” in Table3, the first attribute value of the temporary generalized modelcorresponding to the quasi-identifier “work class”, is “non-enterpriseemployee”. Moreover, the corresponding original value in the secondequivalence class “female, enterprise employee, 33, 1, 3” in Table 3 is“enterprise employee.” In the taxonomy tree shown in FIG. 6B, the parentnode of “non-enterprise employee” and of “enterprise employee” are“employee,” so that “employee” can be set as the second attribute value.

For another example, after the data generalization module 36 processesthe sixth equivalence class “male, private, 43, 4, 4” in Table 3, thefirst attribute value of the temporary generalized model correspondingto the quasi-identifier “work class” is “private”, and the correspondingoriginal value in the seventh equivalence class is “federal-government”.The value “pay” corresponding to the root node of the smallest sub-treeincluding the node corresponding to both the first attribute value andthe original value is set as the second attribute value.

Furthermore, in step S147, when the current temporary generalized modelis stored in the anonymous dataset, the data generalization module 36calculates a total quantity of the equivalence data in the anonymousdata according to the accumulative quantity of each cluster data, andstores the total quantity in the anonymous dataset.

An example of the anonymous dataset including the total quantity ofequivalence data in anonymous data is shown in Table 4 below.

TABLE 4 Sex Work class Age Total quantity Female Employee [33-40] 3Female Local-government 29 3 Male Private [23-43] 3 Male Pay [43-51] 6

As a conclusion according to the anonymous dataset generation device andthe anonymous dataset generation method in the disclosure, the criticalattributes can be set by users according to the usage intentions, andthe equivalence table can be generated according to the criticalattributes and the original dataset. Then, the cluster table conformedto the k-anonymity privacy protection limitation can be generatedaccording to the equivalence classes, and the equivalence classescorresponding to the same cluster code, in the cluster table aregeneralization partially for generating the anonymous dataset.

The generated anonymous dataset can be conformed to the k-anonymityprivacy protection limitation, so that the de-identification may beenough to protect the original data. Because the original valuescorresponding to the critical attributes are maintained, the data withhigher authenticity can be maintained in the anonymous dataset.Furthermore, the partial content is generalized for preventing the lostof data usability caused by the lowered authenticity.

The risk evaluation device of the anonymous dataset and the riskevaluation method of the anonymous dataset are described hereinafter.The risk evaluation device and method of the anonymous dataset canevaluate a degree of de-identification of the anonymous dataset, andfind out the dangerous data which are less de-identified in theanonymous dataset. Briefly, a plurality of sample data can be generatedaccording to the original dataset, and the sample data are used forattacking the anonymous dataset in order to evaluate the risk ofre-identification of the anonymous data.

An example of the original dataset is shown in Table 5 below.

TABLE 5 Original data number Sex Age Postcode 1 Male 22 12111 2 Female25 12111 3 Female 22 12123 4 Male 22 12177 5 Female 25 12189 6 Female 2312128 7 Male 21 12111 8 Female 24 12128 9 Female 23 12123 10 Male 2212128

An example of the anonymous dataset with the total quantity of anonymousdata is shown in Table 6 below. The anonymous parameter used for theanonymous dataset equals 3.

TABLE 6 Sex Age Postcode Total quantity Male [21-22] 121** 4 Female[22-23] 1212*  3 Female [24-25] 121** 3

FIG. 12 is a block diagram of the risk evaluation device of theanonymous dataset according to an embodiment of the disclosure. A riskevaluation device 40 comprises a processor 41 and a memory 48. Thememory 48 is used for storing data or storing data temporarily. Theprocessor 41 comprises a weight generation module 42, a samplegeneration module 44 and a risk evaluation module 46. The processor 41can perform each step of the risk evaluation method for evaluating theanonymous dataset generated according to the original dataset. Theanonymous dataset generation device 30 and the risk evaluation device 40can be embodied in the same processor or computer, or embodied indifferent processors or computers. Furthermore, the original dataset,the anonymous dataset, the weight table and the penetration datasetrequired for performing the risk evaluation method are stored or storedtemporarily in the memory 48, or are stored or stored temporarily in thedata table of the database 50 in the computer.

FIG. 13 is a flow chart of the risk evaluation method of the anonymousdataset according to an embodiment of the disclosure. Firstly, aplurality of appearing times respectively corresponding to the originalvalues of the original dataset is acquired by the weight generationmodule 42 (step S510). The weight generation module 42 searches each ofthe original values, respectively corresponding to thequasi-identifiers, in the original dataset one by one, and sets thenumber of times, which the original values appear in the originaldataset, as the corresponding appearing times. For example, in theoriginal dataset, the quantity of the original data which have theoriginal value “female” and correspond to the quasi-identifier “sex” iscalculated, and the calculated quantity can be set as the appearingtimes corresponding to the original value “female”.

The weight generation module 42 generates a partition set and a weighttable according to a sample parameter, an anonymous parameter and theappearing times (step S520). The sample parameter can be set by theuser, and the risk evaluation device 40 can acquire the sample parameterthrough the GUI. The bigger the sample parameter, the more sample datacan be generated for attacking the anonymous dataset.

FIG. 14 is a flow chart of the step S520 according to an embodiment ofthe disclosure. The weight generation module 42 arranges and groups thequasi-identifiers for generating a plurality of candidate combinations(step S521). Each of the candidate combinations includes at least one ofthe quasi-identifiers. Assume that there are N quasi-identifiers. Theweight generation module 42 firstly generates the candidate combinationseach of which includes only one of the quasi-identifiers, i.e. the Nquasi-identifiers are used for generating one of the candidatecombinations respectively. Then, the weight generation module 42generates the candidate combinations each of which includes two of thequasi-identifiers.

The rest can be deduced by analogy. Therefore, the quantity of thecandidate combinations can be calculated as follows.

${C_{1}^{N} + {C_{2}^{N}\mspace{14mu} \ldots} + C_{N}^{N}} = {\sum\limits_{i = 1}^{N}C_{i}^{N}}$

Assume that the quasi-identifier set is {sex, age, postcode}. Sevencandidate combinations {sex}, {age}, {postcode}, {sex, age}, {sex,postcode}, {age, postcode} and {sex, age, postcode} are generated.

The weight generation module 42 calculates a plurality of original valuecombinational numbers respectively corresponding to the candidatecombinations (step S522). The original value combinational number is agroup quantity obtained by dividing the original data according to thecandidate combinations.

For example, all the original data can only be divided into two groupsaccording to the candidate combination {sex}. The two groups are theoriginal data with the corresponding original value “female” and theoriginal data with the corresponding original value “male” respectively.According to the candidate combination {sex, work class}, all theoriginal data can be divided into a plurality of groups, such as theoriginal data with the corresponding original values “female” and“non-enterprise employee”, the original data with the correspondingoriginal values “female” and “enterprise employee”, or the original datawith the corresponding original values “male” and “private”. Theoriginal value combinational number is a quantity of the groups.

It should be noted that, a combination having original values notexisting in the original dataset is not be used for calculating theoriginal value combinational numbers. Assume that a possible quantity ofthe original values corresponding to one quasi-identifier is three, andthat there are only two original values corresponding to thequasi-identifier, in the original dataset. The original valuecombinational number corresponding to the candidate combination composedof the quasi-identifier is two.

For example, in the Table 5, the original value combinational numberscorresponding to the seven candidate combinations mentioned previouslyare respectively 2 for {sex}, 5 for {age}, 5 for {postcode}, 6 for {sex,age}, 7 for {sex, postcode}, 10 for {age, postcode}, and 10 for {sex,age, postcode}.

In order to control the quantity of the sample data to approach thesample parameter and be not smaller than the sample parameter, theweight generation module 42 selects the smallest original valuecombinational number from at least one of the original valuecombinational number which is bigger than or equal to the sampleparameter, and sets the candidate combination corresponding to theselected original value combinational number, to be a partition set(step S523). Assume the sample parameter is 6. The original valuecombinational number of the candidate combination {sex, age} is 6, whichis equal to the sample parameter, so that the weight generation module42 selects the candidate combination {sex, age} and sets it as thepartition set.

Then, the original dataset is divided according to the partition set,and a weight table is generated according to the sample parameter, theanonymous parameter and the appearing times (step S524). The detail ofthe step S524 is described as follows.

FIG. 15 is a flow chart of the step S524 according to an embodiment ofthe disclosure. The weight generation module 42 calculates a weightparameter according to the anonymous parameter (step S5241). Accordingto an embodiment, a product of the weight parameter and the anonymousparameter is bigger than or equal to the largest appearing time. Theweight generation module 42 sets a smallest integer value as the weightparameter, and a product of the anonymous parameter and the smallestinteger value is bigger than or equal to the largest appearing time. Inother words, the weight parameter is equal to a ceiling function valueof a quotient of the largest appearing times divided by the anonymousparameter. For example, in Table 5, when the appearing time 6corresponding to the original value “female” is the largest appearingtime, and the anonymous parameter is 3, the weight parameter is 2(6/3=2).

Then, the weight generation module 42 reads all the original valuessequentially (step S5242), and determines whether the appearing timecorresponding to the current original value is bigger than the anonymousparameter (step S5243).

When the appearing time corresponding to the current original value isbigger than the anonymous parameter, a weight value corresponding to thecurrent original value is equal to a product of the weight parameter andthe anonymous parameter, minus the appearing time corresponding to thecurrent original value, and plus the sample parameter (step S5244). Whenthe appearing time corresponding to the current original value issmaller than or equal to the anonymous parameter, the weight valuecorresponding to the current original value is equal to a product of theweight parameter and the anonymous parameter, plus the appearing timecorresponding to the current original value, and plus the sampleparameter (step S5245).

For example, the appearing time of the original value “male”corresponding to the quasi-identifier “sex” is 4, which is bigger thanthe anonymous parameter 3. Therefore, the weight value corresponding tothe original value “male” is equal to 8 (2×3−4+6=8). For anotherexample, the appearing time of the original value “23” corresponding tothe quasi-identifier “age” is 2, which is smaller than the anonymousparameter 3. Therefore, the weight value corresponding to the originalvalue “23” is equal to 14 (2×3+2+6=14).

According to the above calculation manner, the weight generation module42 calculates the weight values corresponding to all the original valuesand records the weight values in the weight table. An example of theweight table is shown in Table 7 below.

TABLE 7 Weight Quasi-identifier Original value value Sex Female 8 SexMale 6 Age 21 13 Age 22 8 Age 23 14 Age 24 13 Age 25 14 Postcode 12111 9Postcode 12123 14 Postcode 12128 9 Postcode 12177 13 Postcode 12189 13

After the weight table is generated, the sample generation module 44divides the original dataset into a plurality of data partitionsaccording to the partition set, and generates a penetration datasetaccording to the weight table and the data partitions (step S530). Thepenetration dataset includes a plurality of sample data mentionedpreviously.

FIG. 16 is a flow chart of the step S530 according to an embodiment ofthe disclosure. The sample generation module 44 divides the originaldataset into the data partitions according to the partition set (stepS531). Each of the data partitions includes at least one of the originaldata. The data partitions are obtained by dividing the plurality oforiginal data according to the candidate combinations. Herein, aquantity of the obtained data partitions is the original valuecombinational number corresponding to the partition set. For theprevious example, the original dataset is divided into the six datapartitions respectively having the corresponding original values“female, 22”, “female, 23”, “female, 24”, “female, 25”, “male, 21” and“male, 22”, according to the partition set {sex, age}.

The sample generation module 44 reads all the data partitionssequentially, and calculates an original weight of each of the originaldata in the current data partition via the weight table (step S532). Thesample generation module 44 selects one of the original data accordingto the original weight, and sets the selected original data as a sampledata (step S533).

The original weight of the original data is a total of the weight valuescorresponding to all the original values of the original data. After theoriginal weights of all the original data in the current data partitionare obtained, the original data corresponding to the largest originalweight can be selected and set as the sample data.

For example, the data partition corresponding to “female, 23” includesthe two original data “female, 23, 12123” and “female, 23, 12128”. Theoriginal weight of the original data “female, 23, 12123” is a total ofthe weight values of the original values “female”, “23” and “12123”,which is equal to 36 (8+14+14=36). Similarly, the original weight of theoriginal data “female, 23, 12128” is 31. Because the original weight ofthe original data “female, 23, 12123” is the largest original weight inthe data partition, the sample generation module 44 sets the originaldata “female, 23, 12123” as the sample data.

Then, the sample generation module 44 updates the weight table accordingto the selected original data (i.e. the sample data) (step S534).Whenever the sample generation module 44 has selected the sample data inthe data partition, the sample generation module 44 updates the weighttable according to the newly selected sample data, and then calculatesthe original weight of a next data partition according to the updatedweight table, so as to select a next sample data in the next datapartition.

According to an embodiment, in the step S534, the weight valuecorresponding to the original value of the selected original data issubtracted by 1. For example, the original data “female, 23, 12123” isselected as the sample data, and each of the weight values of theoriginal values “female”, “23” and “12123” is subtracted by 1.

By performing the steps S510 to S530, a plurality of distinctive sampledata can be obtained from the original dataset, and these sample datacan be used for attacking the anonymous dataset which are required toevaluate. The risk evaluation module 46 compares each of the sample datawith the plurality of anonymous data of the anonymous dataset forobtaining a plurality of matching quantities respectively correspondingto these sample data (step S540).

FIG. 17 is a flow chart of the step S540 according to an embodiment ofthe disclosure. The risk evaluation module 46 reads all the sample datasequentially (step S541) and performs below steps S542 to S545 for eachof the sample data.

The risk evaluation module 46 compares the original value of the currentsample data with the third attribute value of the current anonymous dataaccording to the quasi-identifiers for all the anonymous datasequentially (step S542). The risk evaluation module 46 determineswhether each of the corresponding original values and each of the thirdattribute values are in a same attribute level (step S543). When thecorresponding original value and the third attribute value are in thesame attribute level, the risk evaluation module 46 sets the currentanonymous data as a matching data (step S544).

More specifically, the risk evaluation module 46 acquires one of thesample data, and then compares the sample data with each of theanonymous data. The comparisons are repeated until all the sample datahave been compared with the anonymous data. When comparing the sampledata with the anonymous data, the risk evaluation module 46 sets all thequasi-identifiers as a current attribute one by one, and then comparesthe original value corresponding to the current attribute, with thethird attribute value. For all the quasi-identifiers, if each of theoriginal values and each of the third attribute values are in the sameattribute level, it indicates that the sample data is in accordance withthe anonymous data. Therefore, the anonymous data can be set as thematching data.

The third attribute value belonging to the numeric type attribute can bea generalized value range. When the original value of the current sampledata is in the corresponding generalized value range, the original valueof the current sample data and the corresponding third attribute valueare in the same attribute level. For example, when the original value is23, and when the corresponding third attribute value is [23-43], theoriginal value 23 is in the range of [23-43]. Herein, the original valueand the third attribute value are in the same attribute level. Foranother example, the third attribute value is 23, which can be regardedas a range of [23-23]. Therefore, if the corresponding original value isalso 23, the original value and the third attribute value are also inthe same attribute level.

The third attribute value belonging to the categorical type attributecan be a generalized string. When the original value of the currentsample data belongs to the corresponding generalized string, theoriginal value of the current sample data and the corresponding thirdattribute value are in the same attribute level. Similar to the previousdetermining manner for searching the smallest generalized model, if thenode corresponding to the third attribute value, in the taxonomy tree isnot a leaf node, and the node corresponding to the original valuebelongs to a sub-tree having a root node corresponding to the thirdattribute value, the original value and the third attribute value are inthe same attribute level. For example, the original value “12123” andthe third attribute value “121**” are in the same attribute level.Furthermore, if the original value and the third attribute value are thesame, it means that they are in the same attribute level.

After one of the sample data is compared with one of the anonymous data,the risk evaluation module 46 determines whether the current anonymousdata is the last anonymous data in the anonymous dataset (step S545). Ifthe current anonymous data is not the last anonymous data in theanonymous dataset, the step S542 is performed again for reading a nextanonymous data and for the continuous comparison.

On the contrary, when the current anonymous data is the last anonymousdata in the anonymous dataset, a quantity of the matching datacorresponding to the current sample data can be set as the correspondingmatching quantity (step S546). Then, the risk evaluation module 46returns to the step S541 for reading a next sample data until thematching quantities of all the sample data are calculated.

The risk evaluation module 46 calculates and outputs a risk evaluationresult according to the matching quantities (step S550). The riskevaluation result includes a maximum risk probability, a minimum riskprobability or an average risk probability. According to an embodiment,a reciprocal of the smallest matching quantity can be set as the maximumrisk probability, a reciprocal of the largest matching quantity can beset as the minimum risk probability, and a reciprocal of an averagevalue of the matching quantities can be set as the average riskprobability. According to another embodiment, a product of the matchingquantity and a quantity of the original data of the corresponding datapartition, divided by the total quantity of the original data of theoriginal dataset, can be set as the average risk probability.

The risk evaluation device 40 provides the risk evaluation result to theadministrator through the GUI, so that the administrator can determinewhether the anonymous dataset can be opened for users under the currentrisk level. Furthermore, the matching data found via the sample data canbe provided to the administrator for a reference. If the administratorconsiders that the risk is relative higher, the anonymous parameter orthe quasi-identifiers can be modified, and the anonymous datasetgeneration device 30 can be used for re-generating a new anonymousdataset. If the administrator accepts the de-identification result ofthe anonymous dataset, the current anonymous dataset can be opened forusers to perform research or other purposes.

As a conclusion according to the anonymous dataset generation method anddevice of the disclosure, the equivalence table can be generatedaccording to the critical attributes representing the users' intentions;therefore the users' intentions have already been taken intoconsiderations before generating the cluster table. And, by employingthe methods for sorting the equivalence table according to the attributesequence and for generating the data clusters according to the anchorattributed original values, the extent of data being modified in thedata generalization can be reduced. Therefore, by employing theanonymous dataset generation method and device, not only that highauthenticity of the critical attributed data can be maintained, theanonymous dataset with a low distortion can be generated under higherexecution efficiency.

Furthermore, the risk evaluation method and device of the anonymousdataset of the disclosure can perform the risk evaluation ofre-identification for the de-identified anonymous dataset. The riskevaluation method and device of the anonymous dataset generate theweight table and divide the database according to the times each of theoriginal values appears in the original dataset, and perform the riskevaluation for the dangerous sample data sampled from each of the datapartitions. Because the quantity of the sample data is far smaller thanthe total quantity of the original data in the original dataset, thecomputational quantity and processing time required in the riskevaluation can be reduced substantially.

Note that the specifications relating to the above embodiments should beconstrued as exemplary rather than as limitative of the disclosure, withmany variations and modifications being readily attainable by a personof average skill in the art without departing from the spirit or scopethereof as defined by the appended claims and their legal equivalents.

What is claimed is:
 1. An anonymous dataset generation method,comprising: acquiring a critical attribute set and a quasi-identifierset, wherein the critical attribute set comprises at least one criticalattribute, the quasi-identifier set comprises a plurality ofquasi-identifiers, and one of the at least one critical attribute or oneof the quasi-identifiers is set as an anchor attribute; generating anequivalence table according to the quasi-identifier set, the criticalattribute set and an original dataset, wherein the equivalence tablecomprises a plurality of equivalence classes, each of the equivalenceclasses comprises at least one equivalence data, and each equivalencedata comprises a plurality of original values corresponding to thequasi-identifiers respectively; generating a plurality of data clustersof a cluster table sequentially according to the equivalence table,wherein each of the data clusters comprises at least one of theequivalence classes; and generalizing content of the cluster table togenerate and output an anonymous dataset corresponding to the originaldataset, wherein the original values corresponding to the anchorattribute are maintained originally in the anonymous dataset.
 2. Theanonymous dataset generation method according to claim 1, wherein thestep of acquiring the critical attribute set and the quasi-identifierset comprises: reading the quasi-identifier set and the criticalattribute set; and deleting all the at least one critical attribute,which does not belong to the quasi-identifier set, from the criticalattribute set when any one of the at least one critical attribute doesnot belong to the quasi-identifier set.
 3. The anonymous datasetgeneration method according to claim 1, wherein the step of generatingthe equivalence table according to the quasi-identifier set, thecritical attribute set and the original dataset, comprises: extractingthe equivalence table from the original dataset according to thequasi-identifier set, wherein each of the equivalence classes comprisesa corresponding quantity of the at least one equivalence data;generating an attribute sequence according to the quasi-identifier setand the critical attribute set; generating a plurality of value codescorresponding to the original values; encoding the equivalence classesaccording to the attribute sequence and the value codes, to generate aplurality of equivalence codes; and sorting the equivalence classes ofthe equivalence table according to the equivalence codes, and outputtingthe sorted equivalence table.
 4. The anonymous dataset generation methodaccording to claim 3, wherein the step of generating the value codescorresponding to the original values comprises: for at least one of thequasi-identifiers belonging to a numeric type attribute, setting thecorresponding original values as the corresponding value codes; and foreach one of the quasi-identifiers belonging to a categorical typeattribute, generating a taxonomy tree according to the correspondingoriginal values, and encoding the corresponding original values via thetaxonomy tree to obtain the corresponding value codes.
 5. The anonymousdataset generation method according to claim 4, wherein each of the atleast one critical attribute is one of the quasi-identifiers, and formsthe attribute sequence through an attribute sequence rule, and theattribute sequence rule comprises a first rule, a second rule, a thirdrule and a fourth rule; the first rule requires that a priority of theat least one critical attribute is higher than a priority of the atleast one quasi-identifier which does not belong to the at least onecritical attribute; the second rule requires that a priority of thequasi-identifier of the categorical type attribute is higher than apriority of the quasi-identifier of the numeric type attribute; thethird rule requires that a priority of the quasi-identifiercorresponding to a lower height of the taxonomy tree is higher than apriority of the quasi-identifier corresponding to a higher height of thetaxonomy tree; the fourth rule requires that a priority of thequasi-identifier corresponding to a lower original value variance ishigher than a priority of the quasi-identifier corresponding to a higheroriginal value variance; and the critical attribute or thequasi-identifier, which has the highest priority, is set as the anchorattribute.
 6. The anonymous dataset generation method according to claim1, wherein each of the equivalence classes comprises a correspondingquantity of the at least one equivalence data, and the step ofgenerating the cluster table according to the equivalence tablecomprises: adding the equivalence classes to the data clusterssequentially according to the corresponding quantities; and when a totalquantity of the plurality of equivalence data in any one of the dataclusters is smaller than an anonymous parameter, performing steps of:setting the data cluster, which the total quantity of the plurality ofequivalence data is smaller than the anonymous parameter, as a firstcluster; setting the data cluster before the first cluster, as a secondcluster; and merging the first cluster and the second cluster when theoriginal values corresponding to the anchor attribute, in the firstcluster and the second cluster are the same.
 7. The anonymous datasetgeneration method according to claim 6, wherein the step of adding theequivalence classes to the data clusters according to the correspondingquantities sequentially comprises: reading the equivalence classessequentially; and when the read equivalence class is the first in theequivalence table, performing steps of: adding a temporary cluster, andsetting the temporary cluster to correspond to one of the data clusters;recording the original value corresponding to the anchor attribute, inthe read equivalence class to be a current anchor value; accumulating anaccumulative quantity according to the corresponding quantity of theread equivalence class; and adding the read equivalence class to thetemporary cluster according to the anonymous parameter and theaccumulative quantity.
 8. The anonymous dataset generation methodaccording to claim 7, wherein the step of adding the equivalence classesto the data clusters sequentially according to the correspondingquantities further comprises: when the read equivalence class is not thefirst in the equivalence table, and when the original valuecorresponding to the anchor attribute, in the read equivalence class isthe same as the current anchor value, performing steps of: accumulatingthe accumulative quantity according to the corresponding quantity of theread equivalence class; and adding the read equivalence class to thetemporary cluster according to the anonymous parameter and theaccumulative quantity.
 9. The anonymous dataset generation methodaccording to claim 8, wherein the step of adding the equivalence classesto the data clusters sequentially according to the correspondingquantities further comprises: when the read equivalence class is not thefirst in the equivalence table, and when the original valuecorresponding to the anchor attribute, in the read equivalence class isnot the same as the current anchor value, performing steps of: storingthe temporary cluster as the corresponding data cluster; initializingthe temporary cluster and the accumulative quantity, and setting theinitialized temporary cluster to correspond to a next data cluster afterthe stored data cluster; adding the read equivalence class to theinitialized temporary cluster according to the anonymous parameter andthe corresponding quantity; accumulating the initialized accumulativequantity according to the corresponding quantity of the read equivalenceclasses; and recording the original value corresponding to the anchorattribute, in the read equivalence class as the current anchor value.10. The anonymous dataset generation method according to claim 8,wherein the step of adding the read equivalence class to the temporarycluster according to the anonymous parameter and the accumulativequantity comprises: adding all the at least one equivalence data of theread equivalence class to the temporary cluster when the accumulativequantity is smaller than the anonymous parameter.
 11. The anonymousdataset generation method according to claim 10, wherein the step ofadding the read equivalence class to the temporary cluster according tothe anonymous parameter and the accumulative quantity further comprises:when the accumulative quantity is equal to the anonymous parameter, orwhen the accumulative quantity is bigger than the anonymous parameterand is smaller than twice of the anonymous parameter, performing stepsof: adding all the at least one equivalence data of the read equivalenceclass to the temporary cluster; storing the temporary cluster as thecorresponding data cluster; and initializing the temporary cluster andthe accumulative quantity, and setting the initialized temporary clusterto correspond to a next data cluster after the corresponding datacluster.
 12. The anonymous dataset generation method according to claim11, wherein the anonymous parameter is a positive integer bigger than 1,the corresponding quantity of the read equivalence classes is biggerthan the anonymous parameter, and the step of adding the readequivalence class to the temporary cluster according to the anonymousparameter and the accumulative quantity further comprises: when theaccumulative quantity is bigger than or equal to twice of the anonymousparameter, performing steps of: dividing all the plurality ofequivalence data of the read equivalence class into a first group and asecond group, wherein the first group comprises at least one of theplurality of equivalence data, and the second group comprises at leastone of the remaining equivalence data in the read equivalence class;adding the at least one equivalence data of the first group to thetemporary cluster; storing the temporary cluster as the correspondingdata cluster; initializing the temporary cluster and the accumulativequantity, and setting the initialized temporary cluster to correspond toa next data cluster after the corresponding data cluster; adding the atleast one equivalence data of the second group to the initializedtemporary cluster; storing the temporary cluster with the second group,as the corresponding data cluster; and initializing the temporarycluster and the accumulative quantity, and setting the initializedtemporary cluster to correspond to a next data cluster after thecorresponding data cluster with the second group.
 13. The anonymousdataset generation method according to claim 1, wherein each of the dataclusters comprises a cluster code and at least one of the equivalenceclasses, and the step of generalizing the content of the cluster tableto generate and output the anonymous dataset corresponding to theoriginal dataset, comprises: reading the equivalence classes of the dataclusters sequentially; when the read equivalence class is the first inthe cluster table, setting the read equivalence class as a temporarygeneralized model, wherein the temporary generalized model comprises aplurality of first attribute values corresponding to thequasi-identifiers respectively, and the original values of the firstattribute values are the original values of first one of the dataclusters; when the read equivalence class is not the first in thecluster table, and when the read equivalence class is the same as thecluster code corresponding to the temporary generalized model,performing steps of: searching for a smallest generalized model betweenthe read equivalence class and the temporary generalized model; andstoring the smallest generalized model as a updated temporarygeneralized model; and when the read equivalence class is not the firstin the cluster table, and when the read equivalence class is differentfrom the cluster code corresponding to the temporary generalized model,performing steps of: storing the temporary generalized model in theanonymous dataset; and setting the read equivalence class as thetemporary generalized model.
 14. The anonymous dataset generation methodaccording to claim 13, wherein the smallest generalized model comprisesa plurality of second attribute values corresponding to thequasi-identifiers respectively, and the step of searching for thesmallest generalized model between the read equivalence class and thetemporary generalized model comprises: setting the quasi-identifiers asa current identifier sequentially; setting the first attribute valuecorresponding to the current identifier, to be the second attributevalue of the smallest generalized model when the first attribute valueand the original value, which correspond to the current identifier, arethe same; generating a generalized value range according to the firstattribute value, and the original value, which correspond to the currentidentifier, and setting the generalized value range as the secondattribute value of the smallest generalized model, when the firstattribute value and the original value, which correspond to the currentidentifier, are different and the current identifier belongs to anumeric type attribute; and generating a generalized string according toa taxonomy tree, the first attribute value and the original value, whichcorrespond to the current identifier, and setting the generalized stringas the second attribute value of the smallest generalized model, whenthe first attribute value and the original value, which correspond tothe current identifier, are different and the current identifier belongsto a categorical type attribute.
 15. An anonymous dataset generationdevice, comprising: a memory, for storing data or storing datatemporarily; and a processor, coupled to the memory, and comprising: anequivalence generation module, for performing steps of: acquiring acritical attribute set and a quasi-identifier set, wherein the criticalattribute set comprises at least one critical attribute, thequasi-identifier set comprises a plurality of quasi-identifiers, and oneof the at least one critical attribute or one of the quasi-identifiersis set as an anchor attribute; and generating an equivalence tableaccording to the quasi-identifier set, the critical attribute set and anoriginal dataset, wherein the equivalence table comprises a plurality ofequivalence classes, each of the equivalence classes comprises at leastone equivalence data, and each equivalence data comprises a plurality oforiginal values corresponding to the quasi-identifiers respectively; acluster generation module, for generating a plurality of data clustersof a cluster table according to the equivalence table sequentially,wherein each of the data clusters comprises at least one of theequivalence classes; and a data generalization module, for generalizingcontent of the cluster table to generate and output an anonymous datasetcorresponding to the original dataset, wherein the original valuescorresponding to the anchor attribute are maintained originally in theanonymous dataset.
 16. The anonymous dataset generation device accordingto claim 15, wherein in the step of acquiring the critical attribute setand the quasi-identifier set, the equivalence generation module performssteps of: reading the quasi-identifier set and the critical attributeset; and deleting all the at least one critical attribute, not belongingto the quasi-identifier set, from the critical attribute set when anyone of the at least one critical attribute does not belong to thequasi-identifier set.
 17. The anonymous dataset generation deviceaccording to claim 15, wherein in the step of generating the equivalencetable according to the quasi-identifier set, the critical attribute setand the original dataset, the equivalence generation module performssteps of: extracting the equivalence table from the original datasetaccording to the quasi-identifier set, wherein each of the equivalenceclasses comprises a corresponding quantity of the at least oneequivalence data; generating an attribute sequence according to thequasi-identifier set and the critical attribute set; generating aplurality of value codes corresponding to the original values; encodingthe equivalence classes according to the attribute sequence and thevalue codes to generate a plurality of equivalence codes; and sortingthe equivalence classes of the equivalence table according to theequivalence codes, and outputting the sorted equivalence table.
 18. Theanonymous dataset generation device according to claim 17, wherein inthe step of generating the value codes corresponding to the originalvalues, the equivalence generation module performs steps of: for the atleast one of the quasi-identifiers belonging to a numeric typeattribute, setting the corresponding original values as thecorresponding value codes; and for each one of the quasi-identifiersbelonging to a categorical type attribute, generating a taxonomy treeaccording to the corresponding original values, and encoding thecorresponding original values via the taxonomy tree to obtain thecorresponding value codes.
 19. The anonymous dataset generation deviceaccording to claim 18, wherein each of the at least one criticalattribute is one of the quasi-identifiers, the equivalence generationmodule employs an attribute sequence rule to generate the attributesequence, and the attribute sequence rule comprises a first rule, asecond rule, a third rule and a fourth rule; the first rule requiresthat a priority of the at least one critical attribute is higher than apriority of the at least one quasi-identifier which does not belong tothe at least one critical attribute; the second rule requires that apriority of the quasi-identifier of the categorical type attribute ishigher than a priority of the quasi-identifier of the numeric typeattribute; the third rule requires that a priority of thequasi-identifier corresponding to a shorter height of the taxonomy treeis higher than a priority of the quasi-identifier corresponding to ataller height of the taxonomy tree; the fourth rule requires that apriority of the quasi-identifier corresponding to a lower original valuevariance is higher than a priority of the quasi-identifier correspondingto a higher original value variance; and the first critical attribute orthe first quasi-identifier, which has the highest priority, is set asthe anchor attribute.
 20. The anonymous dataset generation deviceaccording to claim 15, wherein each of the equivalence classes comprisesa corresponding quantity of the at least one equivalence data, and thecluster generation module performs steps of: adding the equivalenceclasses to the data clusters according to the corresponding quantitiessequentially; and when a total quantity of the equivalence data in anyone of the data clusters is smaller than an anonymous parameter, thecluster generation module performing steps of: setting the data cluster,which the total quantity of the equivalence data is smaller than theanonymous parameter, as a first cluster; setting the data cluster beforethe first cluster, as a second cluster; and merging the first clusterand the second cluster when the original values corresponding to theanchor attribute, in the first cluster and the second cluster are thesame.
 21. The anonymous dataset generation device according to claim 20,wherein the cluster generation module performs steps of: reading theequivalence classes sequentially; and when the read equivalence class isthe first in the equivalence table, the cluster generation moduleperforming steps of: adding a temporary cluster, and setting thetemporary cluster to correspond to one of the data clusters; recordingthe original value corresponding to the anchor attribute, in the readequivalence class to be a current anchor value; accumulating anaccumulative quantity according to the corresponding quantity of theread equivalence class; and adding the read equivalence class to thetemporary cluster according to the anonymous parameter and theaccumulative quantity.
 22. The anonymous dataset generation deviceaccording to claim 21, wherein the cluster generation module furtherperforms steps of: when the read equivalence class is not the first inthe equivalence table, and when the original value corresponding to theanchor attribute, in the read equivalence class is the same as thecurrent anchor value, the cluster generation module performing steps of:accumulating the accumulative quantity according to the correspondingquantity of the read equivalence class; and adding the read equivalenceclass to the temporary cluster according to the anonymous parameter andthe accumulative quantity.
 23. The anonymous dataset generation deviceaccording to claim 22, wherein the cluster generation module furtherperforms steps of: when the read equivalence class is not the firstequivalence class in the equivalence table, and when the original valuecorresponding to the anchor attribute, in the read equivalence class isdifferent from the current anchor value, the cluster generation moduleperforming steps of: storing the temporary cluster as the correspondingdata cluster; initializing the temporary cluster and the accumulativequantity, and setting the initialized temporary cluster to correspond toa next data cluster after the corresponding data cluster; adding theread equivalence class to the initialized temporary cluster according tothe anonymous parameter and the corresponding quantity; accumulating theinitialized accumulative quantity according to the correspondingquantity of the read equivalence class; and recording the original valuecorresponding to the anchor attribute, in the read equivalence class tobe the current anchor value.
 24. The anonymous dataset generation deviceaccording to claim 22, wherein in the step of adding the readequivalence class to the temporary cluster according to the anonymousparameter and the accumulative quantity, the cluster generation moduleperforms a step of: adding all the at least one equivalence data of theread equivalence class to the temporary cluster when the accumulativequantity is smaller than the anonymous parameter.
 25. The anonymousdataset generation device according to claim 24, wherein the clustergeneration module further performs steps of: when the accumulativequantity is equal to the anonymous parameter, or when the accumulativequantity is bigger than the anonymous parameter and is smaller thantwice of the anonymous parameter, the cluster generation moduleperforming steps of: adding all the at least one equivalence data of theread equivalence class to the temporary cluster; storing the temporarycluster as the corresponding data cluster; and initializing thetemporary cluster and the accumulative quantity, and setting theinitialized temporary cluster to correspond to a next data cluster afterthe corresponding data cluster.
 26. The anonymous dataset generationdevice according to claim 25, wherein the anonymous parameter is apositive integer bigger than 1, the corresponding quantity of the readequivalence class is bigger than the anonymous parameter, and thecluster generation module further performs steps of: when theaccumulative quantity is bigger than or equal to twice of the anonymousparameter, the cluster generation module performing steps of: dividingall the equivalence data of the read equivalence class into a firstgroup and a second group, the first group comprises at least one of theequivalence data, the second group comprises at least one of theremaining equivalence data in the read equivalence class; adding the atleast one equivalence data of the first group to the temporary cluster;storing the temporary cluster as the corresponding data cluster;initializing the temporary cluster and the accumulative quantity, andsetting the initialized temporary cluster to correspond to a next datacluster after the stored data cluster; adding the at least oneequivalence data of the second group to the initialized temporarycluster; storing the temporary cluster with the second group, as thecorresponding data cluster; and initializing the temporary cluster andthe accumulative quantity, and setting the initialized temporary clusterto correspond to a next data cluster after the data cluster stored withthe second group.
 27. The anonymous dataset generation device accordingto claim 15, wherein each of the data clusters comprises a cluster codeand at least one of the equivalence classes, and the data generalizationmodule performs steps of: reading the equivalence classes of the dataclusters sequentially; when the read equivalence class is the first inthe cluster table, setting the first equivalence class as a temporarygeneralized model, wherein the temporary generalized model comprises aplurality of first attribute values corresponding to thequasi-identifiers respectively, and original values of the firstattribute values are the original values of first one of the dataclusters; when the read equivalence class is not the first in thecluster table, and when the read equivalence class is the same as thecluster code corresponding to the temporary generalized model, the datageneralization module performing steps of: searching for a smallestgeneralized model between the read equivalence class and the temporarygeneralized model; and storing the smallest generalized model as anupdated temporary generalized model; and when the read equivalence classis not the first in the cluster table, and when the read equivalenceclass is different from the cluster code corresponding to the temporarygeneralized model, the data generalization module performing steps of:storing the current temporary generalized model in the anonymousdataset; and setting the read equivalence class as the temporarygeneralized model.
 28. The anonymous dataset generation device accordingto claim 27, wherein the smallest generalized model comprises aplurality of second attribute values corresponding to thequasi-identifiers respectively, and the data generalization modulefurther performs steps of: setting the quasi-identifiers as a currentidentifier sequentially; setting the first attribute value correspondingto the current identifier, as the second attribute value of the smallestgeneralized model, when the first attribute value and the originalvalue, which correspond to the current identifier, are the same;generating a generalized value range according to the first attributevalue and the original value, which correspond to the currentidentifier, and setting the generalized value range as the secondattribute value of the smallest generalized model, when the firstattribute value and the original value, which correspond to the currentidentifier, are different and the current identifier belongs to anumeric type attribute; and generating a generalized string according toa taxonomy tree, the first attribute value and the original value, whichcorrespond to the current identifier, and setting the generalized stringas the second attribute value of the smallest generalized model, whenthe first attribute value and the original value, which correspond tothe current identifier, are different and the current identifier belongsto a categorical type attribute.
 29. A risk evaluation method, forevaluating an anonymous dataset generated according to an originaldataset, and comprising: acquiring a plurality of appearing timesrespectively corresponding to a plurality of original values of theoriginal dataset; generating a partition set and a weight tableaccording to a sample parameter, an anonymous parameter and theappearing times; dividing the original dataset into a plurality of datapartitions according to the partition set, and generating a penetrationdataset according to the weight table and the data partitions, whereinthe penetration dataset comprises a plurality of sample data; comparingeach sample data with a plurality of anonymous data of the anonymousdataset to obtain a plurality of matching quantities respectivelycorresponding to the sample data; and calculating and outputting a riskevaluation result according to the matching quantities.
 30. The riskevaluation method according to claim 29, wherein the anonymous datasethas a quasi-identifier set, the quasi-identifier set comprises aplurality of quasi-identifiers, the original dataset comprises theoriginal values corresponding to the quasi-identifiers, and theappearing times are times the corresponding original values appear inthe original dataset.
 31. The risk evaluation method of the anonymousdataset according to claim 30, wherein the step of generating thepartition set and the weight table according to the sample parameter,the anonymous parameter and the appearing times comprises: arranging thequasi-identifiers to generate a plurality of candidate combinations,wherein each of the candidate combinations comprises at least one of thequasi-identifiers; calculating a plurality of original valuecombinational numbers respectively corresponding to the candidatecombinations; selecting the smallest original value combinational numberfrom at least one of the original value combinational numbers, which isbigger than or equal to the sample parameter, and setting the candidatecombination corresponding to the selected original value combinationalnumber, to be the partition set; and generating the weight tableaccording to the sample parameter, the anonymous parameter and theappearing times.
 32. The risk evaluation method according to claim 31,wherein the weight table comprises a plurality of weight valuesrespectively corresponding to the original values, and the step ofgenerating the weight table according to the sample parameter, theanonymous parameter and the appearing times comprises: calculating aweight parameter, wherein a product of the weight parameter and theanonymous parameter is bigger than or equal to the largest one of theappearing times; reading the original values sequentially; when theappearing times corresponding to the current original value is biggerthan the anonymous parameter, the weight value corresponding to thecurrent original value being equal to the product of the weightparameter and the anonymous parameter, minus the appearing timecorresponding to the current original value, and plus the sampleparameter; and when the appearing times corresponding to the currentoriginal value is smaller than or equal to the anonymous parameter, theweight value corresponding to the current original value being equal tothe product of the weight parameter and the anonymous parameter, plusthe appearing times corresponding to the current original value, andplus the sample parameter.
 33. The risk evaluation method according toclaim 29, wherein a quantity of the data partitions is bigger than orequal to the sample parameter.
 34. The risk evaluation method accordingto claim 29, wherein the step of dividing the original dataset into thedata partitions according to the partition set and generating thepenetration dataset according to the weight table and the datapartitions, comprises: dividing the original dataset into the datapartitions according to the partition set, wherein each of the datapartitions comprises at least one original data; reading the datapartitions sequentially, and calculating an original weight of each ofthe at least one original data in the current data partition via theweight table; selecting one of the at least one original data from thecurrent data partition according to the original weight, and setting theselected original data as one of the sample data; and updating theweight table according to the selected original data.
 35. The riskevaluation method according to claim 34, wherein the anonymous datasetcomprises a quasi-identifier set, the quasi-identifier set comprises aplurality of quasi-identifiers, each original data comprises theoriginal values respectively corresponding to the quasi-identifiers, theoriginal values respectively correspond to a plurality of weight valuesof the weight table, and the step of updating the weight table accordingto the selected original data comprises: subtracting the weight valuescorresponding to the selected original data by
 1. 36. The riskevaluation method according to claim 29, wherein the anonymous datasetcomprises a quasi-identifier set, the quasi-identifier set comprises aplurality of quasi-identifiers, each sample data comprises the originalvalues respectively corresponding to the quasi-identifiers, eachanonymous data comprises a plurality of third attribute valuesrespectively corresponding to the quasi-identifiers, and the step ofcomparing each sample data with the plurality of anonymous data of theanonymous dataset to obtain the matching quantities respectivelycorresponding to the sample data, comprises: reading the plurality ofsample data sequentially, and for each sample data, performing steps of:comparing the original values of the current sample data with the thirdattribute values of the current anonymous data according to thequasi-identifiers for the plurality of anonymous data sequentially;setting the current anonymous data as a matching data when each originalvalue and each third attribute value, which correspond to each other,are in a same attribute level; and setting a quantity of the matchingdata corresponding to the current sample data, to be the correspondingmatching quantity.
 37. The risk evaluation method according to claim 36,wherein each of the third attribute values belonging to a numeric typeattribute is a generalized value range, when the original value of thecurrent sample data is in the corresponding generalized value range, theoriginal value of the current sample data and the corresponding thirdattribute value are at the same attribute level; and each of the thirdattribute values belonging to a categorical type attribute is ageneralized string, when the original value of the current sample databelongs to the corresponding generalized string, the original value ofthe current sample data and the corresponding third attribute value areat the same attribute level.
 38. The risk evaluation method according toclaim 29, wherein the risk evaluation result comprises a maximum riskprobability, a minimum risk probability or an average risk probability.39. A risk evaluation device for evaluating an anonymous datasetgenerated according to an original dataset, comprising: a memory, forstoring data or storing data temporarily; and a processor, coupled tothe memory, and comprising: a weight generation module, for acquiring aplurality of appearing times respectively corresponding to a pluralityof original values of the original dataset, and for generating apartition set and a weight table according to a sample parameter, ananonymous parameter and the appearing times; a sample generation module,for dividing the original dataset into a plurality of data partitionsaccording to the partition set, and for generating a penetration datasetaccording to the weight table and the data partitions, wherein thepenetration dataset comprises a plurality of sample data; and a riskevaluation module, for comparing each sample data with a plurality ofanonymous data of the anonymous dataset in order to obtain a pluralityof matching quantities respectively corresponding to the plurality ofsample data, and for calculating and outputting a risk evaluation resultaccording to the matching quantities.
 40. The risk evaluation deviceaccording to claim 39, wherein the anonymous dataset comprises aquasi-identifier set, the quasi-identifier set comprises a plurality ofquasi-identifiers, the original dataset comprises the original valuescorresponding to the quasi-identifiers, and the appearing times aretimes the corresponding original values appear in the original dataset.41. The risk evaluation device according to claim 40, wherein the weightgeneration module performs steps of: arranging the quasi-identifiers togenerate a plurality of candidate combinations, wherein each of thecandidate combinations comprises at least one of the quasi-identifiers;calculating a plurality of original value combinational numbersrespectively corresponding to the candidate combinations; selecting thesmallest one from at least one of the original value combinationalnumbers bigger than or equal to the sample parameter, and setting thecandidate combination corresponding to the smallest original valuecombinational number, to be the partition set; and generating the weighttable according to the sample parameter, the anonymous parameter and theappearing times.
 42. The risk evaluation device according to claim 41,wherein the weight table comprises a plurality of weight valuesrespectively corresponding to the original values, and in the step ofgenerating the weight table according to the sample parameter, theanonymous parameter and the appearing times, the weight generationmodule performs steps of: calculating a weight parameter, wherein aproduct of the weight parameter and the anonymous parameter is biggerthan or equal to the largest one of the appearing times; reading theoriginal values sequentially; when the appearing times corresponding tothe current original value is bigger than the anonymous parameter, theweight value corresponding to the current original value is equal to theproduct of the weight parameter and the anonymous parameter, minus theappearing time corresponding to the current original value, and plus thesample parameter; and when the appearing times corresponding to thecurrent original value is smaller than or equal to the anonymousparameter, the weight value corresponding to the current original valueis equal to the product of the weight parameter and the anonymousparameter, plus the appearing times corresponding to the currentoriginal value, and plus the sample parameter.
 43. The risk evaluationdevice according to claim 39, wherein a quantity of the data partitionsis bigger than or equal to the sample parameter.
 44. The risk evaluationdevice according to claim 39, wherein the sample generation moduleperforms steps of: dividing the original dataset into the datapartitions according to the partition set, wherein each of the datapartitions comprises at least one original data; reading the datapartitions sequentially, and calculating an original weight of each ofthe at least one original data in the current data partition via theweight table; selecting one of the at least one original data from thecurrent data partition according to the original weight, and setting theselected original data as one of the plurality of sample data; andupdating the weight table according to the selected original data. 45.The risk evaluation device according to claim 44, wherein the anonymousdataset has a quasi-identifier set, the quasi-identifier set comprises aplurality of quasi-identifiers, each original data comprises theoriginal values corresponding to the quasi-identifiers respectively, theoriginal values respectively correspond to the weight values of theweight table, and the sample generation module performs a step of:subtracting the weight values corresponding to the selected originaldata by
 1. 46. The risk evaluation device according to claim 39, whereinthe anonymous dataset has a quasi-identifier set, the quasi-identifierset comprises a plurality of quasi-identifiers, each sample datacomprises the original values respectively corresponding to thequasi-identifiers, each anonymous data comprises a plurality of thirdattribute values respectively corresponding to the quasi-identifiers,and the risk evaluation module performs steps of: reading the pluralityof sample data sequentially, and for each sample data, performing stepsof: comparing the original values of the current sample data with thethird attribute values of the current anonymous data according to thequasi-identifiers for the plurality of anonymous data sequentially;setting the current anonymous data as a matching data when each originalvalue and each third attribute value, which correspond to each other,are at a same attribute level; and setting a quantity of the matchingdata corresponding to the current sample data, to be the correspondingmatching quantity.
 47. The risk evaluation device according to claim 46,wherein each of the third attribute values belonging to a numeric typeattribute is a generalized value range, when the original value of thecurrent sample data is in the corresponding generalized value range, theoriginal value of the current sample data and the corresponding thirdattribute value are at the same attribute level; and each of the thirdattribute values belonging to a categorical type attribute is ageneralized string, when the original value of the current sample databelongs to the corresponding generalized string, the original value ofthe current sample data and the corresponding third attribute value areat the same attribute level.
 48. The risk evaluation device according toclaim 39, wherein the risk evaluation result comprises a maximum riskprobability, a minimum risk probability or an average risk probability.